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Mining evolving customer-product relationships in multi-dimensional space

机译：在多维空间中挖掘不断发展的客户-产品关系

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Previous work on mining transactional database has focused primarily on mining frequent Itemsets, association rules, and sequential patterns. However, interesting relationships between customers and items, especially their evolution with time, have not been studied thoroughly. In this paper, we propose a Gaussian transformation-based regression model that captures time-variant relationships between customers and products. Moreover, since it is interesting to discover such relationships in a multi-dimensional space, an efficient method has been developed to compute multi-dimensional aggregates of such curves in a data cube environment. Our experimental results have demonstrated the promise of the approach.
机译：以前在挖掘事务数据库方面的工作主要集中在挖掘频繁项集，关联规则和顺序模式上。但是，尚未深入研究顾客与物品之间的有趣关系，尤其是它们随时间的演变。在本文中，我们提出了一个基于高斯变换的回归模型，该模型可以捕获客户和产品之间的时变关系。此外，由于在多维空间中发现这样的关系很有趣，因此已经开发了一种有效的方法来在数据立方体环境中计算此类曲线的多维集合。我们的实验结果证明了该方法的前景。

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